图学习想换新思路可以看这个：TopoFormer用扫描把拓扑结构变成token，喂给Transformer就行，比传统持久同调快还准。
TopoFormer是一个轻量且可扩展的图表示学习框架，核心模块Topo-Scan通过节点或边过滤将图分解为有序的拓扑token序列。这些序列捕捉从局部模体到全局组织的多尺度结构，并交给Transformer生成图级嵌入。与依赖持久同调的管线不同，Topo-Scan完全可并行化，且省去昂贵的图计算。论文在多个图分类和分子性质预测基准上达到与强GNN基线和拓扑方法持平或更优的结果，并提供了拓扑编码稳定性的理论保证。
TopoFormer: Topology Meets Attention for Graph Learning
We introduce Topoformer, a lightweight and scalable framework for graph representation learning that encodes topological structure into attention-friendly sequences. At the core of our method is Topo-Scan, a novel module that decomposes a graph into a short, ordered sequence of topological tokens by slicing over node or edge filtrations. These sequences capture multi-scale structural patterns, from local motifs to global organization, and are processed by a Transformer to produce expressive graph-level embeddings. Unlike traditional persistent homology pipelines, Topo-Scan is parallelizable, avoids costly diagram computations, and integrates seamlessly with standard deep learning architectures. We provide theoretical guarantees on the stability of our topological encodings and demonstrate state-of-the-art performance across graph classification and molecular property prediction benchmarks. Our results show that Topoformer matches or exceeds strong GNN and topology-based baselines while offering predictable and efficient compute. This work opens a new path for parallelizable and unifying approaches to graph representation learning that integrate topological inductive biases into attention frameworks.